Ignition diagnostic system for internal combustion engine (ice) and method of operating the same

Abstract

An ignition diagnostic system and method for evaluating and optimizing the ignition performance of internal combustion engines (ICEs), such as those that burn hydrogen (H2) and/or other fuels. According to one example, the ignition diagnostic system and method use an artificial intelligence (AI) model with one or more machine learning (ML) algorithm(s) to identify, predict and/or otherwise evaluate abnormal combustion events, and to provide such information to an electronic control unit (ECU) in real-time. The ECU, in turn, can make combustion-related modifications to one or more components of the ignition system to optimize performance. By integrating the AI model into the ignition diagnostic system and method, a fast and simplified solution is provided that can be implemented across a wide variety of ICEs, including those that burn alternative fuels, where detecting abnormal combustion events and monitoring combustion-related parameters can be important.

1 . An ignition diagnostic method for use with an internal combustion engine (ICE), comprising the steps of:
 providing an artificial intelligence (AI) model utilizing at least one machine learning (ML) algorithm, the AI model is configured to correlate information from diagnostic signals with combustion-related parameters;   receiving diagnostic signals from an ignition coil assembly that is associated with a cylinder in the ICE;   obtaining information from the received diagnostic signals;   inputting the obtained information into the AI model;   using the AI model and the inputted information to predict combustion-related parameters for the cylinder; and   using the predicted combustion-related parameters to determine if an abnormal combustion event has occurred in the cylinder.   
     
     
         2 . The ignition diagnostic method of  claim 1 , wherein the internal combustion engine (ICE) is a hydrogen (H 2 ) engine. 
     
     
         3 . The ignition diagnostic method of  claim 1 , wherein the providing step further comprises providing an artificial intelligence (AI) model that is configured to correlate information from diagnostic signals in the form of diagnostic signal lengths (Diag) with combustion-related parameters in the form of in-cylinder pressure values (P). 
     
     
         4 . The ignition diagnostic method of  claim 3 , wherein the diagnostic signal lengths (Diag) include information that is representative of both a charging phase of the ignition coil assembly and a discharging phase of the ignition coil assembly. 
     
     
         5 . The ignition diagnostic method of  claim 4 , wherein the information representative of a charging phase of the ignition coil assembly corresponds to an amount of time taken for a charging current to exceed a charging current threshold and is representative of a state or a condition of a primary winding in the ignition coil assembly. 
     
     
         6 . The ignition diagnostic method of  claim 4 , wherein the information representative of a discharging phase of the ignition coil assembly corresponds to an amount of time taken for a discharging current to fall below a discharging current threshold and is representative of a state or a condition of a secondary winding in the ignition coil assembly or of a corresponding spark plug. 
     
     
         7 . The ignition diagnostic method of  claim 1 , wherein the at least one machine learning (ML) algorithm predicts a maximum in-cylinder pressure (P max ), and the predicted maximum in-cylinder pressure (P max ) is used to determine if an abnormal combustion event has occurred in the cylinder. 
     
     
         8 . The ignition diagnostic method of  claim 1 , wherein the at least one machine learning (ML) algorithm predicts in-cylinder pressure trends (P cyl trend ), the predicted in-cylinder pressure trends (P cyl trend ) are used to compute a maximum in-cylinder pressure (P max ), and the computed maximum in-cylinder pressure (P max ) is used to determine if an abnormal combustion event has occurred. 
     
     
         9 . The ignition diagnostic method of  claim 1 , wherein the first using step further comprises using the AI model and the inputted information to predict a maximum in-cylinder pressure (P max ), and the second using step further comprises comparing the predicted maximum in-cylinder pressure (P max ) to a maximum cylinder pressure threshold to return a binary output, when the binary output is a certain value, the second using step determines that an abnormal combustion event has occurred in the cylinder. 
     
     
         10 . The ignition diagnostic method of  claim 1 , wherein the second using step further comprises comparing a predicted maximum in-cylinder pressure (P max ) to a maximum cylinder pressure threshold and, when the predicted maximum in-cylinder pressure (P max ) is less than the maximum cylinder pressure threshold, determining that an abnormal combustion event has occurred in the cylinder. 
     
     
         11 . The ignition diagnostic method of  claim 1 , wherein the method further comprises evaluating correlations between the diagnostic signals and one or more of the following parameter(s): voltage demand, spark advance, secondary current and/or primary current. 
     
     
         12 . The ignition diagnostic method of  claim 1 , wherein the artificial intelligence (AI) model includes a plurality of machine learning (ML) algorithms, and the output of each of the plurality of ML algorithms is weighted so that the method arrives at an overall determination if an abnormal combustion event has occurred in the cylinder. 
     
     
         13 . The ignition diagnostic method of  claim 1 , wherein the ignition diagnostic method is part of a dual-method approach that combines a simplified analytical framework with a genetic algorithm (GA), the dual-method approach uses a diagnostic signal length (Diag) and one or more parameter(s) provided by an electronic control unit (ECU) to classify combustion events as regular combustion events or abnormal combustion events, and also provides an estimated maximum in-cylinder pressure (P max ). 
     
     
         14 . An ignition diagnostic system for use with an internal combustion engine (ICE), comprising:
 an electronic storage device with an artificial intelligence (AI) model having at least one machine learning (ML) algorithm stored thereon, the AI model is configured to correlate information from diagnostic signals with combustion-related parameters;   wherein the ignition diagnostic system is coupled to an ignition coil assembly that is associated with a cylinder in the ICE and is configured to:
 receive diagnostic signals from the ignition coil assembly; 
 obtain information from the received diagnostic signals; 
 input the obtained information into the AI model; 
 use the AI model and the inputted information to predict combustion-related parameters for the cylinder; and 
 use the predicted combustion-related parameters to determine if an abnormal combustion event has occurred in the cylinder. 
   
     
     
         15 . The ignition diagnostic system of  claim 14 , wherein the internal combustion engine (ICE) is a hydrogen (H 2 ) engine. 
     
     
         16 . The ignition diagnostic system of  claim 14 , wherein the at least one machine learning (ML) algorithm predicts a maximum in-cylinder pressure (P max ), and the predicted maximum in-cylinder pressure (P max ) is used to determine if an abnormal combustion event has occurred in the cylinder. 
     
     
         17 . The ignition diagnostic system of  claim 14 , wherein the at least one machine learning (ML) algorithm predicts in-cylinder pressure trends (P cyl trend ), the predicted in-cylinder pressure trends (P cyl trend ) are used to compute a maximum in-cylinder pressure (P max ), and the computed maximum in-cylinder pressure (P max ) is used to determine if an abnormal combustion event has occurred. 
     
     
         18 . The ignition diagnostic system of  claim 14 , wherein the at least one machine learning (ML) algorithm predicts a maximum in-cylinder pressure (P max ), compares the predicted maximum in-cylinder pressure (P max ) to a maximum cylinder pressure threshold to return a binary output, and when the binary output is a certain value, determines that an abnormal combustion event has occurred in the cylinder. 
     
     
         19 . The ignition diagnostic system of  claim 14 , wherein the ignition diagnostic system is further configured to compare a predicted maximum in-cylinder pressure (P max ) to a maximum cylinder pressure threshold and, when the predicted maximum in-cylinder pressure (P max ) is less than the maximum cylinder pressure threshold, determine that an abnormal combustion event has occurred in the cylinder.